GPU Optimization

A deep dive into the FBTriton kernel design for Table Batched Embedding (TBE) operators, exploring how to scale high-performance recommendation systems.

A deep dive into the architecture, performance benchmarks, and decision frameworks for choosing between vLLM and Ollama for LLM inference in 2026.

Discover how AirLLM enables large language model inference on consumer hardware by bypassing VRAM limitations through layer-wise loading and disk-to-GPU streaming.

An in-depth analysis of the hidden costs behind underutilized GPU infrastructure and how developers can leverage API aggregation to maximize ROI.

Discover how building a custom GPU-resident Top-K CUDA kernel eliminates PCIe transfer bottlenecks in Agentic RAG pipelines, delivering microsecond-level retrieval for high-performance LLM applications.

Discover how Hearth, a new Kubernetes Operator, solves the problem of idle GPU costs by enabling scale-to-zero for LLMs like DeepSeek and Qwen across NVIDIA and Ascend hardware.

A deep dive into Google's Gemma 4 release, focusing on GPU requirements, VRAM management, and the revolutionary MoE architecture for local LLM deployment.

A comprehensive guide to optimizing vLLM for production, covering memory budgeting, failure mode diagnostics, and architectural deep dives for stable LLM serving.

Explore why the fundamental difference between compute-bound prefill and memory-bound decode phases necessitates a shift toward disaggregated inference architectures for 2-4x cost savings.

A comprehensive guide for developers and CTOs on reducing Large Language Model (LLM) operational costs through quantization, Kubernetes orchestration, and intelligent API management via n1n.ai.

ScaleOps secures $130M in Series B funding to tackle the growing crisis of GPU shortages and inefficient cloud spending in the AI era through real-time automated infrastructure management.

A comprehensive guide to deploying LLMs locally, covering hardware requirements, quantization techniques, and step-by-step implementation with Ollama and vLLM.